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Charalampos Skoulikaris, Panagiota Venetsanou, Georgia Lazoglou, Christina Anagnostopoulou and Konstantinos Voudouris
Triggering hydrological simulations with climate change gridded datasets is one of the prevailing approaches in climate change impact assessment at a river basin scale, with bias correction and spatio-temporal interpolation being functions routinely used...
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Junjun Cao, Rui Lin, Baoheng Yao, Chunhu Liu, Xiaochao Zhang and Lian Lian
Multifarious and continuous oceanographic observation data is of considerable significance to oceanographic research and military application. In this paper, a novel oceanographic observation method with dual-modal underwater vehicle able to switch betwe...
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Carolynne Hultquist, Zita Oravecz and Guido Cervone
Citizen-led movements producing spatio-temporal big data are potential sources of useful information during hazards. Yet, the sampling of crowdsourced data is often opportunistic and the statistical variations in the datasets are not typically assessed. ...
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Marcos Ruiz-Álvarez, Francisco Alonso-Sarria and Francisco Gomariz-Castillo
Several methods have been tried to estimate air temperature using satellite imagery. In this paper, the results of two machine learning algorithms, Support Vector Machines and Random Forest, are compared with Multiple Linear Regression and Ordinary krigi...
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Claudine Dieulin, Gil Mahé, Jean-Emmanuel Paturel, Soundouss Ejjiyar, Yves Tramblay, Nathalie Rouché and Bouabid EL Mansouri
The African continent has a very low density of rain gauge stations, and long time-series for recent years are often limited and poorly available. In the context of global change, it is very important to be able to characterize the spatio-temporal variab...
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